Better data processing: Isringhausen develops comprehensive equipment overview and database
Isringhausen is a leading supplier of seating systems and springs for the commercial vehicle industry. The company faces the challenge of efficiently bundling and utilizing multi-page and cross-departmental information about its products. This is where the it’s OWL project ‘Data-based product management (product.intelligence)’ comes in. The aim is to develop an overview and standardized database of equipment options that provides a complete overview of the available equipment features and options of the seats and enables a wide range of evaluations. Christoph Kehmeier from Isringhausen explains how this implementation is to succeed in the following interview.
What specific difficulties are there at the moment?
Christoph Kehmeier: “Due to the wide variety of data and databases at ISRI, data access and standardization are extremely complex. Data from different systems can only be analyzed in a very time-consuming process. To do this, the different departments (e.g. Quality, Development, Sales or Purchasing) have to query or request the available data, in some cases from different systems, in order to then process it according to the respective issue.”
What should replace manual evaluation?
Christoph Kehmeier: “Based on various overviews available in the company, an equipment matrix is to be created as the basis for an overview that provides a complete overview of the available equipment features, options and other relevant information on seats.”
What do you hope to achieve with the equipment matrix?
Christoph Kehmeier: “Questions should be processed in a time-optimized manner and colleagues should be able to query corresponding evaluations (e.g. for new developments, quality monitoring or discontinuations) themselves and in a time-optimized manner. In addition, the evaluation options and information are to be successively expanded.”
What challenges need to be overcome?
Christoph Kehmeier: “The challenge is to bring together information from different systems and departments and create a basis that can be built on in the future. In this context, it is of course not only necessary to collect data and its sources, but also to validate it. The requirements can vary greatly depending on the issue at hand.”
